写一段基于pytorch和Yolo的关于识别物品的深度学习的含训练的代码

为了帮助您理解如何使用PyTorch和YOLO构建一个可以识别物品的深度学习模型，并包含训练过程，这里提供了一个简化的例子。请注意，在实际应用中需要对数据集、超参数等做更细致的选择。 首先安装必要的库： ```bash pip install torch torchvision opencv-python matplotlib albumentations ``` 然后按照下面步骤编写代码： ### 导入所需包 ```python import os from pathlib import Path import cv2 import numpy as np import torch from torch.utils.data import DataLoader, Dataset import albumentations as A # 数据增强工具 from albumentations.pytorch.transforms import ToTensorV2 import yolov5 # 假设你已经克隆了yolov5仓库并安装好依赖项 ``` ### 加载预训练YOLOv5模型 ```python # 这里我们加载官方提供的小型版本YOLOv5作为基础架构 model = yolov5.load('yolov5s.pt') device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"Using device: {device}") model.to(device) ``` ### 自定义Dataset类用于读取自定义的数据集 ```python class CustomImageDataset(Dataset): def __init__(self, img_dir, transform=None): self.img_labels = [] # 此处应添加从文件读取标签信息的功能 self.transform = transform image_files = list(Path(img_dir).glob('*.*'))[:10] # 只选取前10张图片进行演示 for file in image_files: label_file = str(file.with_suffix('.txt')) with open(label_file) as f: lines = [line.rstrip().split() for line in f] bboxes = [] classes = [] for obj in lines: x_center, y_center, width, height = float(obj[1]), float(obj[2]), float(obj[3]), float(obj[4]) cls_id = int(obj[0]) + 1 bboxes.append([x_center - (width / 2), y_center - (height / 2), x_center + (width / 2), y_center + (height / 2)]) classes.append(cls_id) self.img_labels.append({ "file": str(file), "bboxes": bboxes, "classes": classes }) def __len__(self): return len(self.img_labels) def __getitem__(self, idx): entry = self.img_labels[idx] img_path = entry['file'] boxes = entry["bboxes"] labels = entry["classes"] img = cv2.imread(img_path)[..., ::-1].copy() transformed = self.transform(image=img, bboxes=boxes, class_labels=labels) transformed_image = transformed['image'] transformed_bboxes = transformed['bboxes'] transformed_class_labels = transformed['class_labels'] targets = {"boxes": [], "labels": []} for box_idx in range(len(transformed_bboxes)): bbox = transformed_bboxes[box_idx] targets["boxes"].append(bbox) targets["labels"].append(torch.tensor(transformed_class_labels[box_idx])) return transformed_image, targets ``` ### 定义图像变换函数（如随机裁剪） ```python transform = A.Compose([ A.Resize(height=640, width=640), A.RandomCrop(width=480, height=480), A.HorizontalFlip(p=0.5), A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensorV2(), ],bbox_params=A.BboxParams(format='pascal_voc',label_fields=['class_labels'])) ``` ### 创建DataLoader实例 ```python dataset_train = CustomImageDataset('./path/to/train/images/', transform=transform) dataloader_train = DataLoader(dataset_train, batch_size=4, shuffle=True) ``` ### 模型训练循环示例 ```python optimizer = torch.optim.Adam(model.parameters(), lr=0.001) num_epochs = 5 for epoch in range(num_epochs): print(f'\nEpoch [{epoch+1}/{num_epochs}]') model.train() running_loss = .0 for images, target_dicts in dataloader_train: optimizer.zero_grad() outputs = model(images.to(device)) losses = {} for i, output_dict in enumerate(outputs): loss_i = yolov5.loss.compute_loss(output_dict, [t[i]['boxes'].to(device) for t in target_dicts], [t[i]['labels'].long().to(device) for t in target_dicts]) losses[f'yolo_layer_{i}'] = loss_i total_loss = sum(losses.values()) total_loss.backward() optimizer.step() running_loss += total_loss.item()*images.size(0) avg_epoch_loss = running_loss/len(dataloader_train.dataset) print(f'Training Loss: {avg_epoch_loss:.4f}') ``` 这段代码只是一个非常简单的框架，实际工程化部署还需要考虑更多因素比如验证集评估指标监控、测试阶段推理性能优化以及分布式训练支持等等。此外，请确保你的环境中已正确配置CUDA/GPU环境以便充分利用硬件加速能力。